Overview

Dataset statistics

Number of variables15
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory124.9 B

Variable types

Numeric14
Categorical1

Alerts

Total_ACSC has constant value "0"Constant
OBJECTID is highly overall correlated with ZIPHigh correlation
ZIP is highly overall correlated with OBJECTIDHigh correlation
Anxiety_Di is highly overall correlated with Mood_Disor and 5 other fieldsHigh correlation
Mood_Disor is highly overall correlated with Anxiety_Di and 6 other fieldsHigh correlation
Alcohol_re is highly overall correlated with Mood_Disor and 3 other fieldsHigh correlation
Diabetes is highly overall correlated with Anxiety_Di and 6 other fieldsHigh correlation
Hypertensi is highly overall correlated with Anxiety_Di and 5 other fieldsHigh correlation
Asthma is highly overall correlated with Anxiety_Di and 6 other fieldsHigh correlation
Discharges is highly overall correlated with Mood_Disor and 6 other fieldsHigh correlation
MH_ER is highly overall correlated with Anxiety_Di and 7 other fieldsHigh correlation
Total_MH is highly overall correlated with Anxiety_Di and 7 other fieldsHigh correlation
SHAPE_Length is highly overall correlated with SHAPE_AreaHigh correlation
SHAPE_Area is highly overall correlated with SHAPE_LengthHigh correlation
OBJECTID has unique valuesUnique
ZIP has unique valuesUnique
Anxiety_Di has unique valuesUnique
Mood_Disor has unique valuesUnique
Alcohol_re has unique valuesUnique
Diabetes has unique valuesUnique
Hypertensi has unique valuesUnique
Asthma has unique valuesUnique
F65_FallsER has unique valuesUnique
Discharges has unique valuesUnique
MH_ER has unique valuesUnique
Total_MH has unique valuesUnique
SHAPE_Length has unique valuesUnique
SHAPE_Area has unique valuesUnique

Reproduction

Analysis started2023-09-16 21:07:27.004648
Analysis finished2023-09-16 21:08:43.566297
Duration1 minute and 16.56 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

OBJECTID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.5
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.0 B
2023-09-16T21:08:43.791497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.25
Q17.25
median13.5
Q319.75
95-th percentile24.75
Maximum26
Range25
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation7.6485293
Coefficient of variation (CV)0.56655772
Kurtosis-1.2
Mean13.5
Median Absolute Deviation (MAD)6.5
Skewness0
Sum351
Variance58.5
MonotonicityStrictly increasing
2023-09-16T21:08:44.143081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 1
 
3.8%
2 1
 
3.8%
25 1
 
3.8%
24 1
 
3.8%
23 1
 
3.8%
22 1
 
3.8%
21 1
 
3.8%
20 1
 
3.8%
19 1
 
3.8%
18 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
1 1
3.8%
2 1
3.8%
3 1
3.8%
4 1
3.8%
5 1
3.8%
6 1
3.8%
7 1
3.8%
8 1
3.8%
9 1
3.8%
10 1
3.8%
ValueCountFrequency (%)
26 1
3.8%
25 1
3.8%
24 1
3.8%
23 1
3.8%
22 1
3.8%
21 1
3.8%
20 1
3.8%
19 1
3.8%
18 1
3.8%
17 1
3.8%

ZIP
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60054.038
Minimum60002
Maximum60099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.0 B
2023-09-16T21:08:44.408395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum60002
5-th percentile60011.25
Q160036.25
median60047.5
Q360080.5
95-th percentile60094.25
Maximum60099
Range97
Interquartile range (IQR)44.25

Descriptive statistics

Standard deviation27.5891
Coefficient of variation (CV)0.00045940458
Kurtosis-0.93179683
Mean60054.038
Median Absolute Deviation (MAD)19.5
Skewness-0.061358159
Sum1561405
Variance761.15846
MonotonicityStrictly increasing
2023-09-16T21:08:44.674257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
60002 1
 
3.8%
60010 1
 
3.8%
60096 1
 
3.8%
60089 1
 
3.8%
60087 1
 
3.8%
60085 1
 
3.8%
60084 1
 
3.8%
60083 1
 
3.8%
60073 1
 
3.8%
60069 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
60002 1
3.8%
60010 1
3.8%
60015 1
3.8%
60020 1
3.8%
60030 1
3.8%
60031 1
3.8%
60035 1
3.8%
60040 1
3.8%
60042 1
3.8%
60044 1
3.8%
ValueCountFrequency (%)
60099 1
3.8%
60096 1
3.8%
60089 1
3.8%
60087 1
3.8%
60085 1
3.8%
60084 1
3.8%
60083 1
3.8%
60073 1
3.8%
60069 1
3.8%
60064 1
3.8%

Anxiety_Di
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256.14039
Minimum108.42067
Maximum530.46505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.0 B
2023-09-16T21:08:45.091100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum108.42067
5-th percentile124.99085
Q1170.64225
median232.42729
Q3299.08563
95-th percentile497.48664
Maximum530.46505
Range422.04438
Interquartile range (IQR)128.44338

Descriptive statistics

Standard deviation121.17991
Coefficient of variation (CV)0.47309958
Kurtosis0.069324243
Mean256.14039
Median Absolute Deviation (MAD)63.072683
Skewness0.97771721
Sum6659.6503
Variance14684.571
MonotonicityNot monotonic
2023-09-16T21:08:45.530791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
393.6089968 1
 
3.8%
132.8350144 1
 
3.8%
294.2595273 1
 
3.8%
169.135565 1
 
3.8%
436.0354895 1
 
3.8%
509.5248809 1
 
3.8%
268.9019138 1
 
3.8%
260.065498 1
 
3.8%
300.6943305 1
 
3.8%
108.4206722 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
108.4206722 1
3.8%
123.4567901 1
3.8%
129.5930109 1
3.8%
132.8350144 1
3.8%
142.4761626 1
3.8%
169.135565 1
3.8%
169.5736434 1
3.8%
173.8480536 1
3.8%
175.294814 1
3.8%
182.4503425 1
3.8%
ValueCountFrequency (%)
530.465048 1
3.8%
509.5248809 1
3.8%
461.3719246 1
3.8%
436.0354895 1
3.8%
393.6089968 1
3.8%
321.2943573 1
3.8%
300.6943305 1
3.8%
294.2595273 1
3.8%
268.9019138 1
3.8%
261.6488435 1
3.8%

Mood_Disor
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean267.11353
Minimum140.54532
Maximum452.20899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.0 B
2023-09-16T21:08:46.004549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum140.54532
5-th percentile155.84804
Q1199.16343
median235.86922
Q3345.38391
95-th percentile429.27155
Maximum452.20899
Range311.66368
Interquartile range (IQR)146.22048

Descriptive statistics

Standard deviation88.566622
Coefficient of variation (CV)0.33156922
Kurtosis-0.5376305
Mean267.11353
Median Absolute Deviation (MAD)47.950092
Skewness0.65956604
Sum6944.9517
Variance7844.0466
MonotonicityNot monotonic
2023-09-16T21:08:46.362798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
359.3224988 1
 
3.8%
153.848463 1
 
3.8%
241.1963338 1
 
3.8%
191.5800949 1
 
3.8%
284.3709714 1
 
3.8%
372.6515919 1
 
3.8%
222.6843974 1
 
3.8%
195.8517948 1
 
3.8%
299.0541797 1
 
3.8%
140.5453158 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
140.5453158 1
3.8%
153.848463 1
3.8%
161.8467903 1
3.8%
188.470787 1
3.8%
188.9541502 1
3.8%
191.5800949 1
3.8%
195.8517948 1
3.8%
209.0983272 1
3.8%
222.6843974 1
3.8%
228.3887176 1
3.8%
ValueCountFrequency (%)
452.2089921 1
3.8%
446.5970562 1
3.8%
377.2950363 1
3.8%
374.0065451 1
3.8%
372.6515919 1
3.8%
359.3224988 1
3.8%
357.6312191 1
3.8%
308.6419753 1
3.8%
299.0541797 1
3.8%
284.3709714 1
3.8%

Alcohol_re
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean262.29633
Minimum96.320555
Maximum765.29543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.0 B
2023-09-16T21:08:46.811989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum96.320555
5-th percentile129.59229
Q1183.85232
median245.90005
Q3284.41934
95-th percentile472.20793
Maximum765.29543
Range668.97487
Interquartile range (IQR)100.56702

Descriptive statistics

Standard deviation133.05314
Coefficient of variation (CV)0.50726269
Kurtosis7.7915429
Mean262.29633
Median Absolute Deviation (MAD)61.759608
Skewness2.4140895
Sum6819.7047
Variance17703.139
MonotonicityNot monotonic
2023-09-16T21:08:47.437709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
270.1776041 1
 
3.8%
177.8638329 1
 
3.8%
246.0202605 1
 
3.8%
183.5641914 1
 
3.8%
218.6496802 1
 
3.8%
494.0563243 1
 
3.8%
308.8170417 1
 
3.8%
96.32055481 1
 
3.8%
250.9430868 1
 
3.8%
184.7167008 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
96.32055481 1
3.8%
123.6740106 1
3.8%
147.3471426 1
3.8%
177.8638329 1
3.8%
178.4458509 1
3.8%
179.2157141 1
3.8%
183.5641914 1
3.8%
184.7167008 1
3.8%
189.7383192 1
3.8%
214.2745831 1
3.8%
ValueCountFrequency (%)
765.295425 1
3.8%
494.0563243 1
3.8%
406.6627627 1
3.8%
326.6857897 1
3.8%
320.9876543 1
3.8%
308.8170417 1
3.8%
289.1665806 1
3.8%
270.1776041 1
3.8%
264.8339507 1
3.8%
252.4455664 1
3.8%

Diabetes
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.954171
Minimum18.072289
Maximum122.27657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.0 B
2023-09-16T21:08:48.336951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18.072289
5-th percentile19.25171
Q138.826936
median50.451713
Q376.536427
95-th percentile118.68879
Maximum122.27657
Range104.20428
Interquartile range (IQR)37.709491

Descriptive statistics

Standard deviation31.517848
Coefficient of variation (CV)0.52569901
Kurtosis-0.50721166
Mean59.954171
Median Absolute Deviation (MAD)16.838034
Skewness0.69384411
Sum1558.8085
Variance993.37477
MonotonicityNot monotonic
2023-09-16T21:08:48.774226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
69.63788301 1
 
3.8%
43.50304521 1
 
3.8%
38.51420746 1
 
3.8%
43.94500596 1
 
3.8%
110.4677739 1
 
3.8%
114.1622017 1
 
3.8%
62.41677763 1
 
3.8%
74.00430571 1
 
3.8%
59.37660684 1
 
3.8%
42.91510612 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
18.07228916 1
3.8%
18.72004792 1
3.8%
20.8466966 1
3.8%
25.99222493 1
3.8%
35.96181509 1
3.8%
38.51420746 1
3.8%
38.70145244 1
3.8%
39.20338717 1
3.8%
42.36735758 1
3.8%
42.91510612 1
3.8%
ValueCountFrequency (%)
122.2765674 1
3.8%
120.1976584 1
3.8%
114.1622017 1
3.8%
110.4677739 1
3.8%
90.11173856 1
3.8%
89.01116686 1
3.8%
77.38046754 1
3.8%
74.00430571 1
3.8%
69.63788301 1
3.8%
62.41677763 1
3.8%

Hypertensi
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.230231
Minimum6.0240964
Maximum142.28546
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.0 B
2023-09-16T21:08:49.079291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.0240964
5-th percentile15.064405
Q118.104976
median28.336799
Q340.701459
95-th percentile75.173207
Maximum142.28546
Range136.26136
Interquartile range (IQR)22.596483

Descriptive statistics

Standard deviation28.285385
Coefficient of variation (CV)0.78071222
Kurtosis7.2112782
Mean36.230231
Median Absolute Deviation (MAD)11.107462
Skewness2.3747223
Sum941.98602
Variance800.06298
MonotonicityNot monotonic
2023-09-16T21:08:49.352760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
20.4817303 1
 
3.8%
19.5013651 1
 
3.8%
25.67613831 1
 
3.8%
20.40303848 1
 
3.8%
57.13850373 1
 
3.8%
66.9751583 1
 
3.8%
41.61118509 1
 
3.8%
16.81916039 1
 
3.8%
34.89140814 1
 
3.8%
23.4082397 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
6.024096386 1
3.8%
14.69125757 1
3.8%
16.18384852 1
3.8%
16.22404153 1
3.8%
16.29513753 1
3.8%
16.81916039 1
3.8%
17.63951251 1
3.8%
19.5013651 1
3.8%
20.40303848 1
3.8%
20.4817303 1
3.8%
ValueCountFrequency (%)
142.2854602 1
3.8%
77.90588969 1
3.8%
66.9751583 1
3.8%
66.08194161 1
3.8%
57.13850373 1
3.8%
49.65762375 1
3.8%
41.61118509 1
3.8%
37.97228024 1
3.8%
34.89140814 1
3.8%
33.01006238 1
3.8%

Asthma
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.742773
Minimum6.0240964
Maximum158.03766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.0 B
2023-09-16T21:08:49.614740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.0240964
5-th percentile6.6306986
Q117.222264
median26.513789
Q340.388083
95-th percentile101.11925
Maximum158.03766
Range152.01357
Interquartile range (IQR)23.165819

Descriptive statistics

Standard deviation35.426175
Coefficient of variation (CV)0.91439441
Kurtosis4.3284753
Mean38.742773
Median Absolute Deviation (MAD)11.729455
Skewness1.9993504
Sum1007.3121
Variance1255.0139
MonotonicityNot monotonic
2023-09-16T21:08:49.871987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
31.40531979 1
 
3.8%
16.50115508 1
 
3.8%
38.51420746 1
 
3.8%
14.12518049 1
 
3.8%
74.91492712 1
 
3.8%
101.4775126 1
 
3.8%
20.80559254 1
 
3.8%
37.00215285 1
 
3.8%
41.01270782 1
 
3.8%
7.802746567 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
6.024096386 1
3.8%
6.240015974 1
3.8%
7.802746567 1
3.8%
14.12518049 1
3.8%
14.43232842 1
3.8%
16.50115508 1
3.8%
16.98813444 1
3.8%
17.92465128 1
3.8%
19.21164452 1
3.8%
20.64557826 1
3.8%
ValueCountFrequency (%)
158.0376619 1
3.8%
101.4775126 1
3.8%
100.0444642 1
3.8%
74.91492712 1
3.8%
60.07449237 1
3.8%
56.64346982 1
3.8%
41.01270782 1
3.8%
38.51420746 1
3.8%
37.97228024 1
3.8%
37.00215285 1
3.8%

F65_FallsER
Real number (ℝ)

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.433339
Minimum9.9418247
Maximum35.399356
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.0 B
2023-09-16T21:08:50.177760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.9418247
5-th percentile10.60877
Q112.893189
median17.802021
Q320.198406
95-th percentile23.106392
Maximum35.399356
Range25.457532
Interquartile range (IQR)7.3052172

Descriptive statistics

Standard deviation5.4121612
Coefficient of variation (CV)0.31044892
Kurtosis3.5250963
Mean17.433339
Median Absolute Deviation (MAD)3.5651011
Skewness1.2974003
Sum453.26681
Variance29.291489
MonotonicityNot monotonic
2023-09-16T21:08:50.483047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
14.94677021 1
 
3.8%
9.941824724 1
 
3.8%
10.2972281 1
 
3.8%
19.72094454 1
 
3.8%
19.22383202 1
 
3.8%
16.80736696 1
 
3.8%
15.61776124 1
 
3.8%
12.04006935 1
 
3.8%
17.9391504 1
 
3.8%
21.00935133 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
9.941824724 1
3.8%
10.2972281 1
3.8%
11.54339635 1
3.8%
12.04006935 1
3.8%
12.45196339 1
3.8%
12.84867862 1
3.8%
12.8600823 1
3.8%
12.99250765 1
3.8%
13.87914914 1
3.8%
14.94677021 1
3.8%
ValueCountFrequency (%)
35.39935623 1
3.8%
23.28343661 1
3.8%
22.57525672 1
3.8%
21.95433381 1
3.8%
21.00935133 1
3.8%
20.29718525 1
3.8%
20.2399789 1
3.8%
20.07368658 1
3.8%
19.72094454 1
3.8%
19.22383202 1
3.8%

Discharges
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9919.7727
Minimum7512.0663
Maximum13394.284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.0 B
2023-09-16T21:08:50.776242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7512.0663
5-th percentile7863.5216
Q18694.7589
median9455.0967
Q311104.271
95-th percentile13196.871
Maximum13394.284
Range5882.2172
Interquartile range (IQR)2409.5119

Descriptive statistics

Standard deviation1716.3015
Coefficient of variation (CV)0.17301823
Kurtosis-0.44831552
Mean9919.7727
Median Absolute Deviation (MAD)1040.2154
Skewness0.74446529
Sum257914.09
Variance2945690.9
MonotonicityNot monotonic
2023-09-16T21:08:51.082363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
9590.103093 1
 
3.8%
8955.508619 1
 
3.8%
8301.67119 1
 
3.8%
7512.066274 1
 
3.8%
11480.19041 1
 
3.8%
12540.81158 1
 
3.8%
11310.10795 1
 
3.8%
8206.680795 1
 
3.8%
10005.47836 1
 
3.8%
11743.72848 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
7512.066274 1
3.8%
7749.135194 1
3.8%
8206.680795 1
3.8%
8240.135205 1
3.8%
8301.67119 1
3.8%
8406.328381 1
3.8%
8613.297151 1
3.8%
8939.144091 1
3.8%
8955.508619 1
3.8%
9021.9925 1
3.8%
ValueCountFrequency (%)
13394.28351 1
3.8%
13381.60398 1
3.8%
12642.67401 1
3.8%
12540.81158 1
3.8%
11743.72848 1
3.8%
11480.19041 1
3.8%
11310.10795 1
3.8%
10486.75914 1
3.8%
10150.26498 1
3.8%
10005.47836 1
3.8%

MH_ER
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean708.22228
Minimum388.93642
Maximum1574.8836
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.0 B
2023-09-16T21:08:51.398663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum388.93642
5-th percentile439.8451
Q1531.87416
median651.37644
Q3767.08816
95-th percentile1199.2697
Maximum1574.8836
Range1185.9472
Interquartile range (IQR)235.21401

Descriptive statistics

Standard deviation264.03356
Coefficient of variation (CV)0.37281171
Kurtosis3.8537048
Mean708.22228
Median Absolute Deviation (MAD)127.35582
Skewness1.7947049
Sum18413.779
Variance69713.721
MonotonicityNot monotonic
2023-09-16T21:08:51.722506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
837.2762806 1
 
3.8%
388.9364193 1
 
3.8%
714.5441389 1
 
3.8%
574.4396883 1
 
3.8%
902.4038826 1
 
3.8%
1240.648554 1
 
3.8%
635.7534506 1
 
3.8%
511.7029474 1
 
3.8%
697.2691488 1
 
3.8%
508.9748223 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
388.9364193 1
3.8%
420.9990289 1
3.8%
496.3832974 1
3.8%
503.2657438 1
3.8%
508.9748223 1
3.8%
511.7029474 1
3.8%
520.1958384 1
3.8%
566.9091101 1
3.8%
574.4396883 1
3.8%
575.7513167 1
3.8%
ValueCountFrequency (%)
1574.883633 1
3.8%
1240.648554 1
3.8%
1075.133209 1
3.8%
911.3312512 1
3.8%
902.4038826 1
3.8%
837.2762806 1
3.8%
774.9074844 1
3.8%
743.630201 1
3.8%
714.5441389 1
3.8%
697.2691488 1
3.8%

Total_MH
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1161.9286
Minimum733.96974
Maximum2344.1104
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.0 B
2023-09-16T21:08:52.050902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum733.96974
5-th percentile772.77835
Q1920.92651
median1052.3709
Q31335.3495
95-th percentile1779.9302
Maximum2344.1104
Range1610.1406
Interquartile range (IQR)414.42303

Descriptive statistics

Standard deviation369.75013
Coefficient of variation (CV)0.31822103
Kurtosis2.9997708
Mean1161.9286
Median Absolute Deviation (MAD)224.22985
Skewness1.525855
Sum30210.144
Variance136715.16
MonotonicityNot monotonic
2023-09-16T21:08:52.393298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1452.376054 1
 
3.8%
733.9697406 1
 
3.8%
1095.031356 1
 
3.8%
908.2018725 1
 
3.8%
1319.481307 1
 
3.8%
1839.352008 1
 
3.8%
1212.159408 1
 
3.8%
799.4606049 1
 
3.8%
1264.009622 1
 
3.8%
770.9914468 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
733.9697406 1
3.8%
770.9914468 1
3.8%
778.1390421 1
3.8%
799.4606049 1
3.8%
815.5499238 1
3.8%
908.2018725 1
3.8%
911.6177071 1
3.8%
948.8529278 1
3.8%
959.6585894 1
3.8%
962.5527426 1
3.8%
ValueCountFrequency (%)
2344.11037 1
3.8%
1839.352008 1
3.8%
1601.664722 1
3.8%
1506.278873 1
3.8%
1452.376054 1
3.8%
1351.877825 1
3.8%
1340.638955 1
3.8%
1319.481307 1
3.8%
1264.009622 1
3.8%
1212.159408 1
3.8%

Total_ACSC
Categorical

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size336.0 B
0
26 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 26
100.0%

Length

2023-09-16T21:08:52.740959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-16T21:08:53.040278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 26
100.0%

Most occurring characters

ValueCountFrequency (%)
0 26
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26
100.0%

SHAPE_Length
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean146142.98
Minimum25907.372
Maximum247258.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size336.0 B
2023-09-16T21:08:53.350215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum25907.372
5-th percentile49650.164
Q1104885.08
median162289.93
Q3179788.25
95-th percentile213923.6
Maximum247258.22
Range221350.85
Interquartile range (IQR)74903.173

Descriptive statistics

Standard deviation55334.678
Coefficient of variation (CV)0.37863384
Kurtosis-0.33383785
Mean146142.98
Median Absolute Deviation (MAD)39617.815
Skewness-0.45944768
Sum3799717.5
Variance3.0619266 × 109
MonotonicityNot monotonic
2023-09-16T21:08:53.653132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
215525.1552 1
 
3.8%
178637.1096 1
 
3.8%
62444.47166 1
 
3.8%
164174.7444 1
 
3.8%
163122.6118 1
 
3.8%
137038.9478 1
 
3.8%
159182.3679 1
 
3.8%
164753.2176 1
 
3.8%
209118.9368 1
 
3.8%
146555.4274 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
25907.37247 1
3.8%
45385.39523 1
3.8%
62444.47166 1
3.8%
90452.89559 1
3.8%
92363.08463 1
3.8%
92539.70873 1
3.8%
102713.1381 1
3.8%
111400.8906 1
3.8%
115796.7238 1
3.8%
137038.9478 1
3.8%
ValueCountFrequency (%)
247258.2247 1
3.8%
215525.1552 1
3.8%
209118.9368 1
3.8%
207743.9126 1
3.8%
196071.5737 1
3.8%
188925.3902 1
3.8%
180171.9624 1
3.8%
178637.1096 1
3.8%
176981.8594 1
3.8%
164753.2176 1
3.8%

SHAPE_Area
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.7252285 × 108
Minimum-1.1490621 × 109
Maximum-17809434
Zeros0
Zeros (%)0.0%
Negative26
Negative (%)100.0%
Memory size336.0 B
2023-09-16T21:08:53.975895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.1490621 × 109
5-th percentile-9.0091583 × 108
Q1-7.0274448 × 108
median-4.4853543 × 108
Q3-2.1652641 × 108
95-th percentile-68858459
Maximum-17809434
Range1.1312527 × 109
Interquartile range (IQR)4.8621807 × 108

Descriptive statistics

Standard deviation2.9929618 × 108
Coefficient of variation (CV)-0.63340044
Kurtosis-0.64871468
Mean-4.7252285 × 108
Median Absolute Deviation (MAD)2.571131 × 108
Skewness-0.36311259
Sum-1.2285594 × 1010
Variance8.9578205 × 1016
MonotonicityNot monotonic
2023-09-16T21:08:54.318110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-1149062138 1
 
3.8%
-802735362.8 1
 
3.8%
-141373658.4 1
 
3.8%
-227772422.2 1
 
3.8%
-389880041.8 1
 
3.8%
-391907383.9 1
 
3.8%
-505163484.5 1
 
3.8%
-728237916.6 1
 
3.8%
-519658694.9 1
 
3.8%
-170066928.7 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
-1149062138 1
3.8%
-919381804.8 1
3.8%
-845517890.6 1
3.8%
-819008275.9 1
3.8%
-802735362.8 1
3.8%
-730424359.5 1
3.8%
-728237916.6 1
3.8%
-626264179.8 1
3.8%
-621475385.1 1
3.8%
-617591322.9 1
3.8%
ValueCountFrequency (%)
-17809434.34 1
3.8%
-45177572.98 1
3.8%
-139901116.5 1
3.8%
-141373658.4 1
3.8%
-166617676.2 1
3.8%
-170066928.7 1
3.8%
-212777737.9 1
3.8%
-227772422.2 1
3.8%
-257421150.6 1
3.8%
-345792947.4 1
3.8%

Interactions

2023-09-16T21:08:38.718871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:30.297121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:39.860570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:47.143510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:55.891554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:59.973128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:04.429368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:09.552383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:13.265254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:16.708291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:22.146376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:26.493030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:29.923303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:35.229510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:38.951225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:30.801137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:40.260216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:47.942930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:56.186568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:00.203880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:04.829047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:09.819650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:13.518487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:17.093027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:22.363739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:26.746724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:30.246761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:35.465353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:39.201510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:31.510240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:40.713176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:48.520835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:56.486617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:00.425527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:05.318338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:10.106682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:13.744680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:17.548377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:22.576431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:26.979526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:30.546302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:35.699275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:39.429163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:31.949816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:41.152337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:48.919820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:57.106490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:00.657069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:05.727118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:10.393421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:13.954520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:17.968868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:23.360544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:27.212836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:30.847813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:35.907026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:39.676382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:32.399420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:41.704897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:49.440984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:57.411180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:00.907712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:06.242333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:10.687832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:14.203859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:18.439965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:23.635170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:27.463728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:31.322371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:36.202697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:40.391193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:32.916248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:42.238406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:50.092654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:57.730376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:01.154830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:06.674852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:10.989538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:14.444712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:18.913545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:23.928271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:27.700114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:31.768247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:36.527620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:40.639758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:33.545139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:42.692607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:50.990477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:58.044043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:01.446522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:07.048336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:11.275514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:14.675105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:19.375974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:24.201189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:27.938262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:32.234987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:36.794012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:40.879189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:34.185270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:43.458495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:51.835801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:58.295581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:01.763403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:07.346288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:11.564224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:14.918207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:19.828397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:24.499806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:28.208665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:32.637177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:37.063017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:41.141539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:35.183906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:44.110144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:52.551927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:58.543083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:02.108819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:07.660287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:11.811855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:15.185117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:20.298329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:24.791830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:28.468578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:33.071808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:37.314708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:41.377055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:35.993196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:44.609857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:52.988578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:58.759642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:02.423163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:07.935694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:12.061083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:15.415997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:20.732918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:25.078233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:28.691417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:33.461894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:37.545324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:41.619524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:36.913680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:45.105369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:53.434508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:59.009508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:02.719275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:08.243176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:12.291441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:15.661965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:21.103301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:25.356785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:28.932552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:33.858151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:37.769807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:41.856253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:37.748945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:45.591658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:53.989695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:59.236075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:03.103085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:08.471501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:12.550379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:15.905181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:21.387489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:25.647792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:29.181567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:34.288937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:38.016435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:42.110928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:38.749666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:46.144849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:54.709617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:59.486986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:03.510492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:08.731822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:12.791906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:16.174543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:21.676277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:25.941694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:29.434070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:34.722187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:38.253519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:42.331133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:39.140994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:46.598106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:55.611118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:07:59.710844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:03.940380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:09.259544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:13.008063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:16.406681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:21.908369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:26.214936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:29.655802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:34.953679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-16T21:08:38.462055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-16T21:08:54.629133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
OBJECTIDZIPAnxiety_DiMood_DisorAlcohol_reDiabetesHypertensiAsthmaF65_FallsERDischargesMH_ERTotal_MHSHAPE_LengthSHAPE_Area
OBJECTID1.0001.0000.383-0.0280.0060.2960.4070.3220.0350.0900.2250.175-0.0190.172
ZIP1.0001.0000.383-0.0280.0060.2960.4070.3220.0350.0900.2250.175-0.0190.172
Anxiety_Di0.3830.3831.0000.6290.3710.7830.6440.875-0.0050.4090.7350.8080.238-0.185
Mood_Disor-0.028-0.0280.6291.0000.5120.5710.3810.7090.1900.6310.9080.868-0.2120.305
Alcohol_re0.0060.0060.3710.5121.0000.1940.3990.374-0.0390.5230.6440.719-0.1450.166
Diabetes0.2960.2960.7830.5710.1941.0000.6340.8060.2090.5680.6620.6810.194-0.086
Hypertensi0.4070.4070.6440.3810.3990.6341.0000.6570.4690.6030.5610.6240.160-0.041
Asthma0.3220.3220.8750.7090.3740.8060.6571.0000.1120.5400.8040.8190.090-0.023
F65_FallsER0.0350.035-0.0050.190-0.0390.2090.4690.1121.0000.3740.2220.1710.208-0.001
Discharges0.0900.0900.4090.6310.5230.5680.6030.5400.3741.0000.7030.677-0.1380.272
MH_ER0.2250.2250.7350.9080.6440.6620.5610.8040.2220.7031.0000.945-0.1120.244
Total_MH0.1750.1750.8080.8680.7190.6810.6240.8190.1710.6770.9451.000-0.0240.115
SHAPE_Length-0.019-0.0190.238-0.212-0.1450.1940.1600.0900.208-0.138-0.112-0.0241.000-0.891
SHAPE_Area0.1720.172-0.1850.3050.166-0.086-0.041-0.023-0.0010.2720.2440.115-0.8911.000

Missing values

2023-09-16T21:08:42.720392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-16T21:08:43.300937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

OBJECTIDZIPAnxiety_DiMood_DisorAlcohol_reDiabetesHypertensiAsthmaF65_FallsERDischargesMH_ERTotal_MHTotal_ACSCSHAPE_LengthSHAPE_Area
0160002393.608997359.322499270.17760469.63788320.48173031.40532014.9467709590.103093837.2762811452.3760540215525.155184-1.149062e+09
1260010132.835014153.848463177.86383343.50304519.50136516.5011559.9418258955.508619388.936419733.9697410178637.109618-8.027354e+08
2360015142.476163231.371546147.34714318.72004816.2240426.24001619.0793958240.135205496.383297778.1390420111400.890558-3.674576e+08
3460020201.704730452.208992406.66276390.11173966.08194260.07449223.28343713394.283515911.3312511506.278873092363.084625-1.666177e+08
4560030261.648844237.033796250.70882256.95842037.97228037.97228020.29718510486.759142666.9994261156.9072560247258.224672-8.455179e+08
5660031240.857947234.704641178.44585160.97783031.85409029.12373920.2399798939.144091598.628692962.5527430163995.181561-5.271176e+08
6760035129.593011256.951660245.77984825.99222514.69125819.21164518.57920810150.264976688.323223959.6585890115796.723820-3.457929e+08
7860040123.456790308.641975320.98765418.0722896.0240966.02409612.8600829593.675308685.1851851037.037037025907.372467-1.780943e+07
8960042261.025401374.006545214.27458389.01116716.18384956.64347013.8791499951.332561743.6302011351.877825045385.395226-4.517757e+07
91060044182.450343230.644773289.16658135.96181526.15404722.88479112.9925089021.992500575.7513171025.852869092539.708731-2.127777e+08
OBJECTIDZIPAnxiety_DiMood_DisorAlcohol_reDiabetesHypertensiAsthmaF65_FallsERDischargesMH_ERTotal_MHTotal_ACSCSHAPE_LengthSHAPE_Area
161760064530.465048446.597056765.295425122.276567142.285460100.04446412.84867913381.6039761574.8836332344.110370090452.895586-1.399011e+08
171860069108.420672140.545316184.71670142.91510623.4082407.80274721.00935111743.728480508.974822770.9914470146555.427438-1.700669e+08
181960073300.694331299.054180250.94308759.37660734.89140841.01270817.93915010005.478360697.2691491264.0096220209118.936758-5.196587e+08
192060083260.065498195.85179596.32055574.00430616.81916037.00215312.0400698206.680795511.702947799.4606050164753.217612-7.282379e+08
202160084268.901914222.684397308.81704262.41677841.61118520.80559315.61776111310.107949635.7534511212.1594080159182.367883-5.051635e+08
212260085509.524881372.651592494.056324114.16220266.975158101.47751316.80736712540.8115841240.6485541839.3520080137038.947754-3.919074e+08
222360087436.035489284.370971218.649680110.46777457.13850474.91492719.22383211480.190412902.4038831319.4813070163122.611793-3.898800e+08
232460089169.135565191.580095183.56419143.94500620.40303814.12518019.7209457512.066274574.439688908.2018730164174.744392-2.277724e+08
242560096294.259527241.196334246.02026038.51420725.67613838.51420710.2972288301.671190714.5441391095.031356062444.471661-1.413737e+08
252660099461.371925377.295036233.313365120.19765877.905890158.03766222.57525712642.6740101075.1332091601.6647220188925.390246-6.262642e+08